Gene Sets Of Interest Research Articles

Single-cell RNA-sequencing Analysis Reveals the Promoting Role of the Hedgehog Pathway in Epithelial Cells during Cervical Cancer Progression.

This study explored the role of the Hedgehog pathway in epithelial cells during cervical cancer [CC] progression, providing new insights for improving current CC treatment. Abnormal activation of the Hedgehog signaling pathway is associated with the malignant transformation of CC epithelial cells. Single-cell atlas of CC and the role of Hedgehog pathway in epithelial cells during CC progression remain to be explored. To comprehensively analyze the mechanism of Hedgehog pathway activation in CC epithelial cells and its impact on tumor progression by applying single-cell RNA sequencing [scRNA-seq] analysis. The scRNA-seq data were acquired from the Gene Expression Omnibus [GEO] database and then processed with the Seurat package. FindNeighbors and Find- Clusters functions were applied to cluster the cells. The CellMarker database was used for subgroup annotation. Differentially expressed genes [DEGs] in each cell subgroup were filtered by FindAllMarkers function. Biological function analysis for the gene set of interest was performed using Clusterprofiler package. AUCell function was employed to calculate the score of the Hedgehog pathway. The differentiation trajectory in epithelial cell subtypes was generated by performing Pseudotime analysis. Finally, protein-protein network [PPI] was used to investigate the interactions between the Hedgehog pathway and other pathways enriched in the gene set of interest. A total of 9 major cell subpopulations were classified and the proportion of epithelial cells was the highest in CC samples. Further analysis revealed that the Hedgehog pathway was abnormally activated in STYK1+ and TP73+ epithelial cell subtypes. Pseudo-time trajectory analysis showed that the differentiation trajectory of STYK1+ epithelial cells gradually transformed into defense-to-virus cells or into proliferation cells, while TP73+ epithelial cells eventually differentiated into two branches of response to estrogen and virus-induced proliferation. PPI analysis showed that the Hedgehog pathway was involved in the proliferation and viral process of epithelial cells in CC. The current study comprehensively analyzed the features of CC samples and differentiation trajectories of epithelial cell subtypes, as well as the role of the Hedgehog pathway in epithelial cells during CC progression. More importantly, effective target genes were discovered for the molecular diagnosis and precise treatment of CC.

Transcriptional level of inflammation markers associates with short-term brain structural changes in first-episode schizophrenia

BackgroundInflammation has been implicated in the pathology of schizophrenia and may cause neuronal cell death and dendrite loss. Neuroimaging studies have highlighted longitudinal brain structural changes in patients with schizophrenia, yet it is unclear whether this is related to inflammation. We aim to address this question, by relating brain structural changes with the transcriptional profile of inflammation markers in the early stage of schizophrenia.MethodsThirty-eight patients with first-episode schizophrenia and 51 healthy controls were included. High-resolution T1-weighted magnetic resonance imaging (MRI) and clinical assessments were performed at baseline and 2 ~ 6 months follow-up for all subjects. Changes in the brain structure were analyzed using surface-based morphological analysis and correlated with the expression of immune cells-related gene sets of interest reported by previous reviews. Transcriptional data were retrieved from the Allen Human Brain Atlas. Furthermore, we examined the brain structural changes and peripheral inflammation markers in association with behavioral symptoms and cognitive functioning in patients.ResultsPatients exhibited accelerated cortical thickness decrease in the left frontal cortices, less decrease or an increase in the superior parietal lobule and right lateral occipital lobe, and increased volume in the bilateral pallidum, compared with controls. Changes in cortical thickness correlated with the transcriptional level of monocyte across cortical regions in patients (r = 0.54, p < 0.01), but not in controls (r = − 0.05, p = 0.76). In addition, cortical thickness change in the left superior parietal lobule positively correlated with changes in digital span-backward test scores in patients.ConclusionsPatients with schizophrenia exhibit regional-specific cortical thickness changes in the prefrontal and parietooccipital cortices, which is related to their cognitive impairment. Inflammation may be an important factor contributing to cortical thinning in first-episode schizophrenia. Our findings suggest that the immunity-brain-behavior association may play a crucial role in the pathogenesis of schizophrenia.

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria.

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

PyGenePlexus: a Python package for gene discovery using network-based machine learning.

PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes. https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus. Supplementary data are available at Bioinformatics online.

DKK1 affects survival of patients with head and neck squamous cell carcinoma by inducing resistance to radiotherapy and immunotherapy

BackgroundImmune checkpoint inhibitors (ICIs) have been approved to treat various types of tumors, including head and neck squamous cell carcinoma (HNSC). However, most HNSC patients do not respond to ICIs. Radioimmunotherapy has been proposed to enhance the immune response rate in HNSC. Dickkopf-1 (DKK1), a secreted protein, plays important roles in the Wnt signaling pathways. Herein, we aimed to explore the effect of DKK1 on radioimmunotherapy in HNSC. MethodsWe collected the gene expression profile and clinical information of HNSC patients from TCGA and GEO databases. The immune cell infiltration and immune score were assessed using R package CIBERSORT and ESTIMATE. The level of related pathways and biological processes were analyzed by GSEA. The signature scores of gene sets of interest were calculated by GSVA. We also performed cell viability and apoptosis assay, and clonogenic assay to investigate the radiation sensitivity of HSC-3 cells and CNE-2 cells after silencing DKK1 by siRNA. ResultsWe found DKK1 was significantly higher expressed in HNSC, and closely correlated with patients’ survival time, especially the patients who received radiotherapy. DKK1-knockdown HSC-3 cells or CNE-2 cells showed a decrease in cell viability and colony formation, and an increase in apoptotic rate after radiation. DKK1high tumors showed a more immunosuppressive microenvironment with lower infiltration of T cells and higher infiltration of marrow-derived suppressor cells (MDSCs). ConclusionOur data show that DKK1 can affect both radiotherapy and immunotherapy in HNSC, suggesting that DKK1 can be a potential target for radioimmunology in HNSC.

Analysis of G-Quadruplex-Forming Sequences in Drought Stress-Responsive Genes, and Synthesis Genes of Phenolic Compounds in Arabidopsis thaliana.

Sequences of nucleic acids with the potential to form four-stranded G-quadruplex structures are intensively studied mainly in the context of human diseases, pathogens, or extremophile organisms; nonetheless, the knowledge about their occurrence and putative role in plants is still limited. This work is focused on G-quadruplex-forming sites in two gene sets of interest: drought stress-responsive genes, and genes related to the production/biosynthesis of phenolic compounds in the model plant organism Arabidopsis thaliana. In addition, 20 housekeeping genes were analyzed as well, where the constitutive gene expression was expected (with no need for precise regulation depending on internal or external factors). The results have shown that none of the tested gene sets differed significantly in the content of G-quadruplex-forming sites, however, the highest frequency of G-quadruplex-forming sites was found in the 5'-UTR regions of phenolic compounds' biosynthesis genes, which indicates the possibility of their regulation at the mRNA level. In addition, mainly within the introns and 1000 bp flanks downstream gene regions, G-quadruplex-forming sites were highly underrepresented. Finally, cluster analysis allowed us to observe similarities between particular genes in terms of their PQS characteristics. We believe that the original approach used in this study may become useful for further and more comprehensive bioinformatic studies in the field of G-quadruplex genomics.

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates by reducing the influence of genes with greater uncertainty on the estimation of distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes.

Abstract 5009: DKK1 is a potential target for enhancing the efficiency of radioimmunotherapy in head and neck squamous carcinoma

Abstract Immune checkpoint inhibitors (ICIs) have been approved to treat various types of tumors, including head and neck squamous carcinoma (HNSC). However, most HNSC patients do not respond to ICIs. Radioimmunotherapy has been proposed to enhance the immune response rate in HNSC. Herein, we aimed to explore the effect of DKK1 on radiotherapy and immunotherapy in HNSC. We collected the gene expression profile and clinical information of HNSC patients from TCGA, GEO, and ICGC. The immune cell infiltration, level of related pathways and biological processes, signature scores of gene sets of interest were assessed using CIBERSORT, GSEA or GSVA respectively. One of the genes, Dickkopf-1 (DKK1), a secreted protein which plays important roles in the Wnt signaling pathways, was identified because of its significantly higher expression in HNSC. Then, we confirmed the DKK1 expression at protein level by immunohistochemistry of HNSC tissue microarray. CCK8, apoptosis detection and survival curves were performed to investigate the response of DKK1-knockdown HSC-3 cell line to radiation. We found the mRNA and protein expression level of DKK1 was significantly higher in HNSC tissue. The DKK1high group had a shorter survival time in both disease-free survival and overall survival than the DKK1low group. The DKK1high group showed a more immunosuppressive microenvironment with lower infiltration of T cells and higher infiltration of MDSCs. Furthermore, the DKK1high group was enriched in radioresistance related to wound healing, angiogenesis, and autophagy. Patients in the DKK1high group who received radiotherapy had much poorer disease-free survival and overall survival than those in the DKK1low group. HSC-3 cell line was more sensitive to radiation after knockdown of DKK1 by siRNA. Finally, we established a risk score system based on the 9 genes from 41 DKK1-associated immunomodulators and created a nomogram model to predict the prognosis of the patients. Our data show that DKK1 can affect both radiotherapy and immunotherapy in HNSC, suggesting that DKK1 can be a potential target for radioimmunology in HNSC. Citation Format: Xinyu YE, JIAN Zhang, Yi Lu, Yingxuan Gong, Xinyu Jiang, Yueming Li, Yinghao Wang, Xiuzhi Li, Jingwen Liu, Rencui Quan. DKK1 is a potential target for enhancing the efficiency of radioimmunotherapy in head and neck squamous carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5009.

GenePlexus: a web-server for gene discovery using network-based machine learning.

Biomedical researchers take advantage of high-throughput, high-coverage technologies to routinely generate sets of genes of interest across a wide range of biological conditions. Although these technologies have directly shed light on the molecular underpinnings of various biological processes and diseases, the list of genes from any individual experiment is often noisy and incomplete. Additionally, interpreting these lists of genes can be challenging in terms of how they are related to each other and to other genes in the genome. In this work, we present GenePlexus (https://www.geneplexus.net/), a web-server that allows a researcher to utilize a powerful, network-based machine learning method to gain insights into their gene set of interest and additional functionally similar genes. Once a user uploads their own set of human genes and chooses between a number of different human network representations, GenePlexus provides predictions of how associated every gene in the network is to the input set. The web-server also provides interpretability through network visualization and comparison to other machine learning models trained on thousands of known process/pathway and disease gene sets. GenePlexus is free and open to all users without the need for registration.

Relationship Between the Pyroptosis Pathway and Epilepsy: A Bioinformatic Analysis.

PurposeThis study aimed to analyse the correlation between the pyroptosis pathway and epilepsy using bioinformatics analysis technology. We analyzed the expression of gasdermin D (GSDMD) and gasdermin E (GSDME), the key molecules of pyroptosis, in kainic acid-induced epileptic mice.MethodsWeighted gene co-expression network analysis (WGCNA) was used to construct a signed co-expression network from expression data to screen gene sets closely related to epilepsy. The correlation between the module and epilepsy was verified through module conservative analysis, gene ontology (GO) annotation analysis, and correlation analysis with known epilepsy genes. We obtained currently recognized pyroptosis-related molecules through literature review, and correlation analysis was used to evaluate their correlation with epilepsy. Differentially expressed gene (DEG) analysis was used to analyse expression changes of pyroptosis-related molecules at the transcriptome level, compared to the sham group. We subsequently established a kainic acid-induced status epilepticus (SE) model in mice and validated the mRNA and protein expression of GSDMD and GSDME, the key molecules of pyroptosis, by quantitative reverse transcription PCR (qRT-PCR) and western blotting (WB).ResultsUsing WGCNA, module conservative analysis, and correlation analysis with known epilepsy genes, we screened out a module (a gene set of interest) closely related to epilepsy that was prominently enriched in immune and inflammatory-related biological processes. Correlation analysis results suggest that pyroptosis-related molecules are closely related to this module, but have no obvious correlation with others. DEG analysis of molecules associated with pyroptosis suggests that most of the pyroptosis-related molecules had significantly increased expression after SE, such as IL1b, Casp1, Casp4, Pycard, Gsdmd, Nlrp3, Aim2, Mefv, Tlr2, Tlr3, and Tlr4. qRT-PCR and WB analysis confirmed that the mRNA and protein levels of GSDMD in the mouse hippocampus were significantly upregulated after SE. The mRNA expression of GSDME was not different between the epilepsy group and sham group. However, the WB results showed that the expression of full-length GSDME was decreased and GSDME-N-terminus were significantly increased after SE.ConclusionsOur study highlights that the pyroptosis pathway may be closely related to epilepsy. GSDMD and GSDME, the key executive molecules of pyroptosis, will help to understand the pathogenesis of epilepsy and aid in discovering new targets for anti-epileptic drug treatments.

Identification of potential Parkinson’s disease biomarkers using computational biology approaches

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. This aging-related disease occurs due to the degenerative loss of tissue or cellular functions in the brain and due to genetic and epigenetic effects. This study was conducted on an RNA-seq dataset of PD collected from BA9 tissues to get insights to PD. A few RNA-seq based transcriptomics studies on PD are available. However, most of these studies are limited to differential expression analysis, i.e., individual gene-based analysis that ignores interactions and associations among genes to establish the association with the disease. Here, we initially identify differentially expressed genes and then construct a co-expression network on detected genes to identify modules. Module preservation analysis is carried out to find the non-preserved modules. We identify a non-preserved module with 73 (70 are annotated) genes. Differential connectivity analysis, topological analysis, and functional enrichment analysis are performed to find the initial set of interesting genes. Our finding is that 42 (60%) genes are significantly enriched in pathways, biological processes, or molecular functions, and they are topologically interesting. Among these genes, 19 can be linked to the PD based on evidence from literature. They are considered as biomarkers for PD. From the remaining 23 genes, 11 are expressed in brain region. Therefore, these genes may be further explored to understand their roles in PD and can be considered as potential biomarkers.

Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans' Gene Expression Data.

Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. These modules represent responses to changes in the environment (e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g., ATFS-1, DAF-16), genes specific to tissues (e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g., GO term). Analysis of “module-weighted annotations” improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at http://genemodules.org/, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.

Cerebro: interactive visualization of scRNA-seq data.

Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementationThe Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License.Supplementary information Supplementary data are available at Bioinformatics online.

Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures.

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Using singscore to predict mutations in acute myeloid leukemia from transcriptomic signatures.

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Identification of pathways associated with chemosensitivity through network embedding.

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.

9 - LIMITED CONTRIBUTION OF RARE, NONCODING VARIATION TO AUTISM SPECTRUM DISORDER FROM SEQUENCING OF 2,076 GENOMES IN QUARTET FAMILIES

Background Genomic studies in Autism Spectrum Disorder (ASD) have largely focused on high impact de novo mutations that disrupt protein-coding sequence. Given the size of the noncoding genome and its regulatory role in gene function, it is likely that rare, noncoding variation represents an important component of the genetic architecture of ASD. Characterization of noncoding mutations may also provide insights into the cell types and critical developmental stages involved in ASD. However, there is no obvious noncoding equivalent to protein-truncating variants, thus necessitating an unbiased and statistically rigorous association strategy for noncoding variation that parallels standards set by common variant analyses. Methods Using whole genome sequencing (WGS) of 2,076 individuals (519 cases, unaffected sibling controls, and both parents), we evaluated genomic variation across the spectrum of variant size and frequency. The results of 12 variant discovery algorithms were integrated to identify de novo SNVs, indels, and structural variants (SVs); cross-site validation exceeded 93% for all variant classes. Variants were annotated using an extensive series of noncoding functional annotations and gene sets, including gene-defined regions (e.g. promoters) and regulatory regions (e.g. histone marks), conservation, proximity to gene sets of interest (e.g. FMRP targets), and variant type (e.g. SNVs, indels), resulting in 51,801 combinations of annotation categories. ASD association within each category was assessed using a binomial test to compare variant counts in cases and controls in a Category-Wide Association Study (CWAS). To account for multiple testing, correlations of p-values were assessed between the 51,801 categories from 20,000 sets of simulated variants. Eigenvalue decomposition estimated that 4,211 effective tests explained 99% of the variation. Results A small excess of de novo noncoding variants was observed in cases (1.02-fold), which was not significant after correcting for paternal age. While we identified several variants that were likely causal for an individual, no annotation category was significant after correcting for 4,211 tests in the CWAS. The lowest p-values were observed in coding regions, including missense variation and SVs not detected by previous technologies. Similarly, no category of rare inherited variants demonstrated parental transmission bias or ASD association. Discussion Our results suggest that there is no clear category of rare noncoding variation with equivalent impact on ASD risk as large SVs or protein-disrupting mutations. Furthermore, given the multiple categories of noncoding mutations that may potentially confer ASD risk, identifying noncoding disease associations and quantifying this risk will require a statistically rigorous approach that includes stringent multiple testing correction for this multitude of plausible hypotheses. Analogous to genome-wide association studies of common variation, this approach is likely to identify sound and replicable noncoding associations, but will require substantially larger sample sizes, likely in excess of 5,000 cases.

CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.